In conventional 3-D seismic surveying, seismic data is acquired along closely spaced lines which provide detailed subsurface information. With such high-density coverage, large volumes of digital data must be recorded, stored and processed prior to interpretation. The processing requires extensive computer resources. When the seismic data has been processed it is interpreted in the form a 3-D cube (or seismic data volume) which effectively represents a display of subsurface features. The information within the cube can be displayed in various forms, such as horizontal time slice maps, vertical slices or sections in any direction.
In traditional seismic interpretation, one or more seismic events is identified and tracked to yield a set of seismic horizons. Together these horizons are used to form the structural framework of the subsurface in two-way time or depth as the case may be. All subsequent geological modelling and most of today's seismic inversion schemes rely heavily on this framework. For example, seismic attributes can be extracted around an interpreted horizon and used to characterise a reservoir unit.
The task of seismic imaging areas of the earth's crust requires a series of complex physical and simulated processes. For several reasons, these processes deviate from ideal imaging conditions and therefore the seismic images tend to be degraded or distorted.
A challenge in the seismic imaging field is therefore to monitor the quality and conditions at all stages of the imaging process and finally to evaluate the quality of the seismic images to be used in building geological models of the subsurface.
Distortions of imaging conditions such as instabilities, inaccuracies or technological limitations may have quite complicated degradation effects on seismic images. Each step of the imaging process will both transform previous degradations in the seismic images and create new ones.
The seismic image quality is generally defined in terms of temporal and spatial resolution, amplitude fidelity and signal to noise ratio. The image qualities can easily be measured on synthetic seismic images, which contain only one simple seismic event. In this case, the qualities can be measured directly. However, in real seismic events, the shape and complexity of the subsurface layers may distort measurements of quality. In addition, numerous different seismic events and noise may interfere and distort the direct quantification of the quality of the seismic data obtained. Human error, inaccuracies and technological limitations will further distort the seismic data and hence the seismic image produced.
The present invention addresses the need to reduce the distortion of the quality measurements.
In some of these techniques measurements following a seismic event are taken within several windows and stacked together vertically. The techniques are therefore less sensitive to lateral variations in the geology and represent an improvement over prior conventional techniques.
It is therefore an object of the present invention to provide a method of seismic data which is particularly sensitive to acquisition errors and external influences, such as human error, inaccuracies and technological limitations. It is a further object of the present invention to provide a method of assessing and reducing distortion and degradation in seismic images caused by these errors.
It is difficult to predict the presence of such influences, which may occur individually or in groups at any one time. These influences occur at the position within the area to be surveyed where the survey equipment, such as the source and receivers, is located at that time. These influences affect all seismic traces locally and lead to errors in the seismic data obtained. The local volume which is established is called a “cobweb.”
After processing the acquired seismic data in a conventional manner, the errors are to some degree migrated outside to nearby traces in the seismic data volume. The size and nature of these “cobwebs” is dependent on such factors as the seismic data acquisition geometry, the geometry of the subsurface and the sound velocities of the subsurface. The size of the cobwebs or cobweb data volumes is in the order of the maximum source receiver offset and the vertical extension is equal to the seismic trace length. The cobwebs may be difficult to identify by eye and even more difficult to detect by standard quality measurements, since the geological footprint may dominate.
According to the present invention, there is provided a method of assessing acquisition errors in a seismic data volume, comprising the steps of: selecting seismic objects as reflectors in a seismic data volume; measuring one or more qualities of the objects; vertically stacking the measured quality; relating the stacked measurements on the basis of any vertical correlation; and identifying any vertically correlated feature as a cobweb.
Effectively, therefore, the method of the present invention includes the steps of using a series of quality measurements made on a series of seismic attributes within a moving seismic volume, preferably normalising the quality measurements in time or depth intervals, and stacking them vertically to reduce the lateral geological footprint of the quality map. The qualities may be, for example, energy, coherency, dominant frequency or any other seismic attribute.
Preferably, the quality seismic image measurements are first normalised before the stacking step. Preferably, the measurements which are stacked are taken from different locations in the seismic cube. The locations may be inside reflectors, faults or other objects.
The selecting, stacking and relating steps may be performed at the same time as the data is acquired. Alternatively, some of these steps may be carried out on previously acquired data.
The method of the present invention is specialised or focused to detect these cobwebs and may use seismic object detection systems as described in WO00/16125 to point at objects where a quality can be measured. The method may use an unlimited number of seismic attributes to be able to point at these objects. Neural networks can perform the classification of the enhanced attributes, either supervised or unsupervised.
Within a seismic data volume, several classes of seismic objects are present, for example, seismic chimneys, faults and reflectors. Preferably, seismic reflectors are used to detect or sample possible cobwebs as they are the easiest attribute to detect and enhance by automated methods and may be the most sensitive to lateral quality variations. Any given seismic data volume will also contain numerous seismic reflectors, and cobwebs are easier to separate from reflectors than any other seismic object. Furthermore, reflectors do not need to migrate to be detectable and can therefore be monitored during acquisition of the seismic data. In this way, data samples which are not recognised as reflectors can be left out of the quality classification process. This greatly simplifies the method of the present invention, and geological footprint is reduced in the quality map.
To make the detection sensitive enough to be able to separate cobwebs from the reflectors and other types of noise, vertical stacking of quality measurements is needed.
Due to the systematic reduction in data qualities along the vertical axis, both due to the absorption of sound in the earth's crust, and limitations in the imaging concept, vertical normalisation of the quality values may be conducted before they are stacked.